code: def test_blockwise_voting_regressor(
      setup, fit_X, fit_y, predict_X, predict_y
  ):
      est = BlockwiseVotingRegressor(LogisticRegression())
      est.fit(fit_X, fit_y)
      estimators = est.estimators_.fetch()
      if not isinstance(fit_X, np.ndarray):
          assert len(estimators) == 4
  
      result = est.predict(predict_X)
      assert result.dtype == np.dtype("float64")
      assert result.shape == (predict_X.shape[0],)
  
      score = est.score(predict_X, predict_y)
      assert isinstance(score.fetch(), float)
  
file_path: mars/mars/learn/ensemble/tests/test_blockwise.py
imports: import numpy
  import pytest
  from sklearn.datasets import make_classification
  from sklearn.linear_model import LogisticRegression
  from ....None import dataframe
  from ....None import tensor
  from ..None import BlockwiseVotingClassifier, BlockwiseVotingRegressor
label_desc: Use image node_id 3 for calling a global function with example usage: test_blockwise_voting_regressor(setup, fit_X, fit_y, predict_X, predict_y) without return types